Coordination Mechanism Research Articles

Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques

Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques. Coordination among actors in the supply chain is essential for ensuring a well-organized, efficient, and effective response to humanitarian crises, ultimately leading to better outcomes for those affected by disasters. Effective coordination ensures that resources, information, and aid are distributed efficiently and promptly to those in need during humanitarian crises. It also helps optimize resource allocation, prevent duplication of efforts, and ensure that aid reaches the right places at the right time. It supports identifying and mitigating risks in the supply chain, such as delays, bottlenecks, or disruptions, which can impact aid delivery. Coordination fosters better communication among stakeholders, enabling them to share information, collaborate on solutions, and make informed decisions. Precise coordination mechanisms help establish accountability among actors, ensuring that responsibilities are defined, monitored, and fulfilled. This document proposes an efficient logistics system capable of providing aid in a rapid and coordinated manner through unsupervised learning and a medoid partitioning algorithm called PAM (Partitioning Around Medoids) that achieves a global optimum for grouping the vulnerable municipalities of Mexico using INECC data. In addition, the PAM algorithm was compared with the P-Median to check the efficiency of the global optimum.

Bayesian estimation of trunk-leg coordination during walking using phase oscillator models

In human walking, the legs and other body parts coordinate to produce a rhythm with appropriate phase relationships. From the point of view for rehabilitating gait disorders, such as Parkinson Disorders, it is important to understand the control mechanism of the gait rhythm. A previous study showed that the antiphase relationship of the two legs during walking is not strictly controlled using the reduction of the motion of the legs during walking to coupled phase oscillators. However, the control mechanisms other than those of the legs remains unknown. In particular, the trunk moves in tandem with the legs and must play an important role in stabilizing walking because it is above the legs and accounts for more than half of the mass of the human body. This study aims to uncover the control mechanism of the leg-trunk coordination in the sagittal plane using the coupled phase oscillators model and Bayesian estimation. We demonstrate that the leg-trunk coordination is not strictly controlled, as well as the interleg coordination.

A smartphone-integrated bimetallic ratiometric fluorescent probe for specific visual detection of tetracycline antibiotics in food samples and latent fingerprinting

Designing and preparing highly sensitive and accurate fluorescent chemosensors for monitoring tetracycline antibiotics remains a challenge. Herein, a fluorescent chemosensor based on lanthanide metal-organic frameworks (Ln-MOFs) is proposed to realize high-precision monitoring by adjusting the ratio of lanthanide ions. Ln-MOFs with good aqueous stability were prepared by a solvothermal method using Eu3+, Tb3+ and the ligand 4,4′,4″-s-triazine-2,4,6-triyltribenzoic acid (H3TATB) in DMF/NMP/H2O. The Ln-MOFs could recognize oxytetracycline (OTC) and doxycycline (DOX), and the detection limits of OTC and DOX were as low as 8.6 and 4.8 nM, respectively. In particular, Eu(1.4 μM)-Tb-MOF sensors were used for visual detection of OTC and DOX in combination with smartphones with detection lines as low as 9.8 nM and 14.2 nM, respectively. Meanwhile, Eu-MOF, Tb-MOF and Eu(1.4 μM)-Tb-MOF can be used for latent fingerprint (LFP) visualization, demonstrating their potential applications in the field of criminal case investigation. The developed probes were successfully applied to determining OTC and DOX in milk, beef and pork with recoveries ranging from 92.0 % to 109.63 % and relative standard deviations (RSDs) ranging from 1.83 % to 4.56 %. Eu(1.4 μM)-Tb-MOF is believed to utilize its lanthanide metal ion coordination and photoinduced electron transfer (PET) mechanism to achieve highly selective and accurate OTC and DOX detection, which is supported by experimental and density functional theory (DFT) calculations.

A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction

Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

An analysis of humanitarian and health aid harmonisation over a decade (2011–2019) of the Syrian conflict

IntroductionAid harmonisation is a key component of donor efforts to make aid more effective by improving coordination and simplifying and sharing information to avoid duplication. This study evaluates the harmonisation...

Super‐resolution reconstruction algorithm for medical images by fusion of wavelet transform and multi‐scale adaptive feature selection

AbstractConventional computed tomography (CT) images often suffer from blurred edges and unclear details. Image super‐resolution methods can significantly enhance CT image quality, thereby improving diagnostic accuracy. To better extract detailed features and enhance the cascading effects of different feature levels, we propose a novel medical image super‐resolution algorithm that integrates discrete wavelet transform and multi‐scale adaptive feature selection. Our approach uses both the low‐resolution image and its high‐frequency component from the frequency domain as network inputs, with the high‐frequency component providing learning supervision, which enhances detail fidelity in reconstruction. Additionally, we introduce a multi‐scale adaptive feature selection module to learn from different layers of CT images and their inter‐layer correlations. Finally, the pixel information is efficiently integrated by a coordinate attention mechanism incorporating the concept of squeeze excitation. Experimental results show that our method outperforms state‐of‐the‐art methods, achieving superior reconstruction at scale factors of 2, 4, and 8, especially at scale factor 8, where it surpasses others by 1.12 in PSNR, 0.0145 in SSIM, and 0.0038 in LPIPS. Visually, our method also delivers more accurate details and better perceptual quality.

Outcomes from a Zoonotic Disease Prioritization workshop using One Health approach in Mozambique, 2018 to 2023

IntroductionAround 75% of (re)emerging infections are of zoonotic origins. The risk of zoonotic transmission in Mozambique is high because approximately 81% of the country’s labor force is involved with agriculture, which represents a vulnerability for more frequent human-animal interaction and risk of spillover events. A One Health Zoonotic Disease Prioritization (OHZDP) workshop was conducted in Mozambique to facilitate coordination and collaboration within and across sectors to prevent, detect, and respond to zoonotic disease threats. Based on the success of this integrated workshop, the stakeholders developed actions whose results have a great impact on animal welfare, environment and improving public health.MethodsIn 2018, representatives from Mozambique’s human, animal, and environmental sectors from government, universities, research institutions and partners used US CDC’s OHZDP Process to prioritize endemic and emerging zoonotic diseases of greatest national concern and develop recommendations and key interventions needed to advance One Health in Mozambique. After the OHZDP workshop, the Mozambique One Health Secretariat used a theory of change methodology to identify activities for implementation from the recommendations of the OHZDP workshop. Since the OHZDP workshop, the Secretariat has monitored progress of activities annually.ResultsMozambique’s priority zoonotic diseases are rabies, zoonotic tuberculosis, salmonellosis, zoonotic avian influenza, trypanosomiasis, brucellosis, and Crimean-Congo hemorrhagic fever. One Health recommendations and interventions to address the priority zoonotic diseases focused on One Health collaboration, communication, and coordination; laboratory; surveillance; preparedness and response; prevention; workforce development; and research. After the OHZDP workshop, Mozambique established One Health coordination mechanisms, developed training courses for surveillance, laboratory diagnosis, outbreak investigation, and preparedness and response for the priority zoonotic diseases, conducted joint research, and developed plans.ConclusionPrioritization of zoonotic diseases is critical as it facilitated the key One Health players in Mozambique to optimize resources, gain a greater understanding of zoonotic diseases, and implement policies and activities that promote multisectoral, interdisciplinary, and transdisciplinary collaboration across human, animal, and environmental sectors to prevent, detect, and respond to public health threats. The success of these activities implemented by the local Government and One Health partners were built from the implementation and momentum from the Mozambique’s OHZDP workshop.

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5′ ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

The Pathways and Mechanisms for Collaborative Innovation in Vocational Education, Higher Education, and Continuing Education

Against the backdrop of rapid global economic development, the demand for education has gradually shifted from quantitative growth to qualitative improvement. Vocational education, higher education, and continuing education are crucial components of China’s education system, each serving different educational functions. However, the collaborative innovation among these three types of education is still in the preliminary exploration stage, with issues such as scattered resources and imperfect coordination mechanisms. This paper systematically reviews relevant domestic and international research and practical experiences, thoroughly analyzes the pathways, models, and mechanisms of collaborative innovation among these three types of education, and proposes practical and operational suggestions based on the actual situation in Xuzhou City. The study finds that collaboration between vocational education, higher education, and continuing education through innovation not only enhances the overall efficiency of educational resources but also promotes regional economic development and social progress. On this basis, this paper proposes specific measures such as building an education resource-sharing platform, promoting a chain-based talent training system, strengthening policy and institutional guarantees, and improving incentive mechanisms, aiming to provide a reference for local education administrators and contribute to the innovative development of education in China.

TTPRNet: A Real-Time and Precise Tea Tree Pest Recognition Model in Complex Tea Garden Environments

The accurate identification of tea tree pests is crucial for tea production, as it directly impacts yield and quality. In natural tea garden environments, identifying pests is challenging due to their small size, similarity in color to tea trees, and complex backgrounds. To address this issue, we propose TTPRNet, a multi-scale recognition model designed for real tea garden environments. TTPRNet introduces the ConvNext architecture into the backbone network to enhance the global feature learning capabilities and reduce the parameters, and it incorporates the coordinate attention mechanism into the feature output layer to improve the representation ability for different scales. Additionally, GSConv is employed in the neck network to reduce redundant information and enhance the effectiveness of the attention modules. The NWD loss function is used to focus on the similarity between multi-scale pests, improving recognition accuracy. The results show that TTPRNet achieves a recall of 91% and a mAP of 92.8%, representing 7.1% and 4% improvements over the original model, respectively. TTPRNet outperforms existing object detection models in recall, mAP, and recognition speed, meeting real-time requirements. Furthermore, the model integrates a counting function, enabling precise tallying of pest numbers and types and thus offering practical solutions for accurate identification in complex field conditions.

Research on the Coordination Mechanism of Fiscal and Monetary Regulation

Research on the Coordination Mechanism of Fiscal and Monetary Regulation

An assessment of Ghana’s pilot of the RTS,S malaria vaccine implementation programme; 2019–2021: a retrospective study

BackgroundIn May 2019, Ghana piloted the introduction of RTS,S malaria vaccine into routine immunization in 42 districts of seven of the 16 regions. The RTS,S malaria vaccine implementation programme (MVIP) post-introduction evaluation (PIE) conducted in Ghana, assessed the immunization system as well as healthcare worker and caregiver experiences during the phase-one rollout but was less expressive on quantitative grading of the respective thematic areas of the vaccine introduction plan. Given the utility of summary statistics in programme evaluation and communication, this follow-up study aimed to provide an overall rating of the country's performance regarding the MVIP .MethodsA retrospective study was conducted from 10th January to 5th February 2024. It involved review of records to assess key thematic areas of the national MVIP plan, using a study tool adapted from the WHO New Vaccine Introduction (NVI) checklist. A composite score ranging from zero to 100 per cent was generated to assess the country's overall performance regarding introduction of the malaria vaccine, rated on a Likert scale as comprehensive, good, fair, and poor.ResultsThe overall performance in the MVIP was rated 78.9% (30/38) corresponding to a grading of “good” on the Likert scale. Performance indicators under thematic areas including policy, national coordination mechanisms, waste management, health worker training, and pharmacovigilance were completely achieved. However, some weaknesses were exhibited in areas such as financial consideration, cold chain, logistics, and vaccine management, and monitoring and evaluation.ConclusionGhana’s MVIP demonstrated remarkable strengths worth leveraging to improve the national immunization programme. The weaknesses observed in some of the thematic areas present opportunities to engage key immunization partners and stakeholders towards aligning efforts to ensure a more robust expansion phase. The lessons from the MVIP may be relevant to areas introducing malaria vaccine irrespective of the product type—RTS,S or R21.

Informal Foreign Currency Market Rate Coordination and Remittance Flows

ABSTRACT An unintended consequence of remittance flows to economically troubled countries may be the exacerbation of parallel foreign currency market activities. Studying clandestine markets is often hobbled by a lack of data on informal market coordination mechanisms. This paper examines the impact of the sudden cessation of an informal foreign currency exchange reference rate in Zimbabwe on inward remittance flows and public attention to money transfer operators. While the reference rate existed, there was bi-directional feedback between black market currency rates and retail diaspora remittance flows, but not with the flows of non-governmental organizations (NGOs). The abolition of the informal reference rate did not stall retail diaspora remittance flows. NGO remittances reacted negatively to the ban, suggesting concerns with government policy. Online attention on money transfer operators increased after the reference rate’s cessation suggesting remitters shifted to formal channels. Accordingly, remittance costs reduced significantly.

Planning for a future free from rebound effects

This paper argues that attempts to mitigate rebound effects within growth-orientated economic systems are self-defeating. This arises because rebound effects contribute to economic expansion and individual 'welfare' improvements (i.e., they are welcome and even desirable) and they flourish in traditional market systems where resource allocation is conducted in an ex-post fashion. As such, in the context of the transition towards more sustainable societies, we suggest that ex-ante economic planning and coordination mechanisms are needed to help eliminate rebound effects. Specifically, we argue that mechanisms adopted in contemporary supply chains demonstrate the technical feasibility of economic planning. Such techniques, framed within a democratic economic planning architecture, could therefore encourage moves towards a future that allows us to live within biophysical limits. An interdisciplinary research agenda is proposed to this end.

Prioritizing Factors to Foster Improvement of Sales Operations in Small- and Medium-Sized Industrial Organizations

Small- and medium-sized companies depend heavily on their internal configuration to achieve their goals, generate profit, and remain competitive. The performance of the sales department is often crucial for this. Decision-makers need to understand how to coordinate the sales force’s operations while considering team members’ communication and commitment. This article presents an approach to prioritize factors that will improve the operations of the sales department in small- and medium-sized companies in the industrial sector. To achieve this, we adopted the soft modeling approach by (1) outlining a conceptual model that identifies the factors that can lead to improvements based on the literature and (2) using the analytical hierarchy process to validate a construct and prioritize the factors. This study is focused on the organizational domain and involves the participation of sixty employees from medium-sized Mexican companies with at least five years of experience. The results indicate that the factors that foster improvement in sales department operations are communication improvement, failure prevention, workload alignment, and adequate integration of human efforts with technology without neglecting coordination and management mechanisms. This article could encourage academics and practitioners to adopt the soft modeling approach to adopt new courses of action based on continuous learning and improve organizational cohesion.

Towards coordinating self-healing instances: Policy-based and non-cooperative game theory-based approaches with location awareness

Self-healing is an essential functionality in Self-Organizing cellular Network (SON). It aims to ensure service continuity by detecting, diagnosing, and compensating network outage after triggering appropriate Cell Outage Compensation (COC) functions according to the outage case. The role of self-healing is increasingly become indispensable, especially in high density network as in 5 G and beyond, in order to maintain the availability and retainability of mobile service and save revenues. However, the essence of COC entails modifications of different Network Control Parameters (NCP) which are under control of SON Functions (SONF) that are related to other self-x functionalities. In addition, it can request different types of compensation functions according to the outage case. Moreover, it requires to harmonize the objectives of the requested stand-alone SONFs over the involved Candidate Cells (CC) in order to re-build a consistent coverage model. Thus, the need for self-coordination is mandatory to guarantee conflict-free outage compensation procedure. In this paper, we propose a novel integrated coordination scheme in order to coordinate Antenna Tilt (AT)-based and Transmission Power (TXP)-based COC functions. Beside their primary targets of conflict avoidance and resolution, we prove that our location-aware coordination mechanisms, which are based on policies and Non-Cooperative Zero-Sum Game (NCZSG) theory, can collaboratively guide greedy stand-alone single-objective COC functions to reach a stable coverage model. The coordination is done even when there is no prior knowledge of the environment or the compensatory algorithms themselves. The choice of optimal Network Control Parameters Values (NCV) is determined according to the function after the coordinator's verification.

ArmSpy++: Enhanced PIN Inference through Video-based Fine-grained Arm Posture Analysis

As one of the most common ways for user authentication, Personal Identification Number (PIN), due to its simplicity and convenience, has suffered from plenty of side-channel attacks, which pose a severe threat to people’s privacy and property. The success of existing attacks is usually built upon the premise of no occlusion between the attacker and the victim’s hand gesture, but it increases the difficulty of launching the attack and the possibility of exposure. To overcome such limitation, we propose ArmSpy++, an improved video-assisted PIN inference attack built upon our previous research, ArmSpy. Specifically, ArmSpy++ employs new modules to leverage more features like the keystroke-induced elbow bending, wrist speed variation, and the spatial relationship between different arm joints, to correctly detect Keystrokes. ArmSpy++ delves into the perspective relationship and natural typing habits to ensure a high success rate of PIN inference. We also re-designed the inferred PIN pattern coordination mechanism to accurately deduce the PINs. By using a pre-trained HigherHRNet model for posture estimation ArmSpy++ eliminates the necessity of additional training. The extensive experiments demonstrate that ArmSpy++ can achieve over \(83.1\% \) average accuracy with 3 attempts and even \(92.5\% \) for some victims, indicating the severity of the threat posed by ArmSpy++.

Impact of Policy Intensity on Carbon Emission Reductions: Based on the Perspective of China’s Low-Carbon Policy

Economic development often results in significant greenhouse gas emissions, contributing to global climate change, which demands immediate attention. Despite implementing various low-carbon policies to promote sustainable economic and environmental progress, current evaluations reveal limitations and deficiencies. Therefore, this study utilizes a dataset detailing policy intensity at a prefecture-level city in China to investigate the impacts of these policies on carbon emission reduction from 2007 to 2022 in 334 prefecture-level cities, employing a fixed-effects model. Additionally, it assesses the policies’ efficacy. The findings indicate a significant negative correlation between China’s low-carbon policies and carbon emissions, supported robustly by multiple tests. Specifically, a one-unit increase in China’s policy intensity correlates with a 0.53-unit reduction in carbon emissions. Furthermore, the heterogeneity analysis shows that variations in urban agglomerations, environmental resource endowments, pollution levels, and low-carbon policy intensities influence the effectiveness of these policies in reducing carbon emissions. This analysis underscores that policy intensity achieves emission reductions through technological innovation, industrial transformation, welfare crowding out, and pollution transfer, with varying impacts across different environmental contexts, pollution levels, and policy intensities. Based on this analysis, we recommend several policies: formulating low-carbon strategies tailored to local conditions, enhancing regional low-carbon policies, establishing cross-regional coordination mechanisms, and so on. These recommendations not only offer valuable policy insights for China but also serve as useful references for the green and sustainable development of other developing countries.

Narrow road extraction from high-resolution remote sensing images: SWGE-Net and MSIF-Net

ABSTRACT Accurate and complete road network extraction plays a critical role in urban planning, street navigation, and emergency response. At present, narrow roads are a main feature in most public road datasets. However, the continuity and boundary completeness of the extraction results for these narrow roads are relatively poor, due to their varied shapes, uneven spatial distribution, and the presence of various interfering elements. To address these issues, this study introduces a novel network, the Self-weighted Global Context Road Extraction Network (SWGE-Net), which integrates a dilate block and an improved coordinate attention mechanism to effectively capture the complex details and spatial information of narrow roads. Furthermore, most public road training datasets often lack labels for very narrow roads, this omission leads to poor extraction results for these roads in test datasets. In order to further improve the extraction capability for unlabeled, extremely narrow roads, this study introduces another network called the Multi-scale Information Fusion Road Extraction Network (MSIF-Net), which uses the same encoders as SWGE-Net and has a special module for merging information at different scales. This module, with a dilate block and pyramid pooling-based decoder, makes the network better at recognizing and combining features of different sizes. Experimental results indicate that SWGE-Net outperforms the baseline network with road IoU scores of 71.57% and 60.67% on the DeepGlobe and CHN6-CUG road datasets, respectively an improvement of 18.51% and 5.40%. Meanwhile, MSIF-Net not only exceeds the baseline in road IoU scores for both datasets, but also achieves the best performance in extracting unlabeled, extremely narrow roads in qualitative experiments.

Insight into the complexation of uranium with Bacillus sp. dwc-2 by multi-spectroscopic approaches: FT-IR, TRLF and XAFS spectroscopies

The micromechanisms of the interactions between uranium and microorganisms, especially chemical bonds and bonding numbers between uranium and microbe are not yet clear. In this work, uranium coordination with functional groups in/on Bacillus sp. dwc-2 has been rationally explored by X-ray absorption fine structure (XAFS), Fourier transform infrared (FT-IR), time-resolved laser-induced fluorescence (TRLF) spectroscopies. The results indicate that under acidic condition, 66.8 % of O atoms in coordination shell of U(VI) equatorial plane are provided by organic phosphoryl groups via monodentate coordination, 18.8 % and 15.5 % are provided by water molecules and carboxyl groups via monodentate and bidentate coordination, respectively. While under alkaline condition, these proportions change to 53.1 %, 34.4 % and 22.0 %, respectively. Under neutral condition, U(VI) is mainly deposited by complexing with inorganic phosphates released by microbial cells. Under anaerobic condition, partial U(VI) can be reduced to U(IV) which primarily complex with phosphorus containing groups via monodentate and bidentate coordination, existing as amorphous solids or coordination polymers. This is the first exploration of the binding mechanism between uranium and microorganisms at a semi-quantitative level under different conditions. Our studies will further enhance the contribute to the understanding of coordination mechanism of uranium with microorganism, and demonstrate the potential bioremediation value of this bacterium.